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1,806 results for “CA”
Expression data in Arabidopsis Col-0 with cold acclimation (CA) treatment and subsequent cold deacclimation (DA) treatment.
GEO Series GSE49796. Arabidopsis thaliana. 15 samples. Type: Expression profiling by array.
scRNA sequencing of BM hematopoietic cells under CA colonization
GEO Series GSE231824. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing; Other.
Steamed Panax Notoginseng Alleviates CA-MRSA USA300-induced Necroptosis in Polymorphonuclear Neutrophils
GEO Series GSE284429. Staphylococcus aureus. 6 samples. Type: Expression profiling by high throughput sequencing.
Blocking of CA-MSC-induced desmoplasia reprograms the tumor immune microenvironment and enhances cancer immunotherapy
GEO Series GSE185013. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing; Other.
CA-125 as a Biomarker in Renal Medullary Carcinoma: Integrated Molecular Profiling, Functional Characterization, and Prospective Clinical Validation [RNA-Seq]
GEO Series GSE284318. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Expression data from human endometrial cancer cell line AN3-CA treated with 300 nM AZD4547
GEO Series GSE61481. Homo sapiens. 6 samples. Type: Expression profiling by array.
Effect of cortistatin A (CA) on enhancer occupancy in CA-sensitive and -insensitive human cell lines
GEO Series GSE65138. Homo sapiens. 53 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
HIV-CRISPR screen of HIV-1 CA-P90A and CA-N74D mutant viruses with the PIKAHIV library
GEO Series GSE140467. Homo sapiens; Human immunodeficiency virus. 12 samples. Type: Other.
Transcriptional profile Lactobacillus plantarum WCFS1 : p-coumaric acid (p-CA) treated cells
GEO Series GSE40126. Lactiplantibacillus plantarum WCFS1; Lactiplantibacillus plantarum. 3 samples. Type: Expression profiling by array.
FAK activity sustains intrinsic and acquired ovarian cancer resistance to platinum chemotherapy [KMF parental, KMF clone KT13 (FAK-/-), KT13 + GFP-FAK, KT13 + GFP-FAK R454, and KT13 + deltaGSK beta-ca
GEO Series GSE129098. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing.
RNA-seq of Drosophila Glioma Model with Ca-α1T and Slo Knockdowns
GEO Series GSE290059. Drosophila melanogaster. 4 samples. Type: Expression profiling by high throughput sequencing.
The absence of a Ca(2+) signal during mouse egg activation can affect development and gene expression patterns
GEO Series GSE5077. Mus musculus. 6 samples. Type: Expression profiling by array.
Next Generation Sequencing of ovarian CA-MSC & MSC Transcriptomes
GEO Series GSE118624. Homo sapiens. 14 samples. Type: Expression profiling by high throughput sequencing.
Coupled nitrate and phosphate availability facilitated the expansion of eukaryotic life at ca. 1.56 Ga
<p>The file includes supplementary data table S1-S3 for the paper titled "Coupled nitrate and phosphate availability facilitated the expansion of eukaryotic life at ca. 1.56 Ga" submitted to 'Journal of Geophysical Research - Biogeosciences'.</p>
Figure 4 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 4 Map of known distribution for I. dabashanensis in gray outlined in black.
Figure 6 from: Wilson CA (2020) Two new species in Iris series Chinenses (Iridaceae) from south-central China. PhytoKeys 161: 41-60. https://doi.org/10.3897/phytokeys.161.55483
Figure 6 Map of known distribution for I. probstii in gray outlined in black.
Figure 1 from: Alvarado AT, Ybañez-Julca R, Muñoz AM, Tejada-Bechi C, Cerro R, Quiñones LA, Varela N, Alvarado CA, Alvarado E, Bendezú MR, García JA (2021) Frequency of CYP2D6*3 and *4 and metabolizer phenotypes in three mestizo Peruvian populations. Pharmacia 68(4): 891-898. https://doi.org/10.3897/pharmacia.68.e75165
Figure 1 Location of the CYP2D6 gene on the human chromosome, exons, CYP2D6*3 and *4 alleles.
CA-SUM pretrained models
<p>This dataset contains <strong>pretrained models of the CA-SUM network architecture for video summarization</strong>, that is presented in our work titled “Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames”, in Proc. ACM ICMR 2022. </p> <p><strong>Method overview:</strong></p> <p>In our ICMR 2022 paper we describe a new method for unsupervised video summarization. To overcome limitations of existing unsupervised video summarization approaches, that relate to the unstable training of Generator-Discriminator architectures, the use of RNNs for modeling long-range frames' dependencies and the ability to parallelize the training process of RNN-based network architectures, the developed method relies solely on the use of a self-attention mechanism to estimate the importance of video frames. Instead of simply modeling the frames' dependencies based on global attention, our method integrates a concentrated attention mechanism that is able to focus on non-overlapping blocks in the main diagonal of the attention matrix, and to enrich the existing information by extracting and exploiting knowledge about the uniqueness and diversity of the associated frames of the video. In this way, our method makes better estimates about the significance of different parts of the video, and drastically reduces the number of learnable parameters. Experimental evaluations using two benchmarking datasets (SumMe and TVSum) show the competitiveness of the proposed method against other state-of-the-art unsupervised summarization approaches, and demonstrate its ability to produce video summaries that are very close to the human preferences. An ablation study that focuses on the introduced components, namely the use of concentrated attention in combination with attention-based estimates about the frames' uniqueness and diversity, shows their relative contributions to the overall summarization performance.</p> <p><strong>File format:</strong></p> <p>The “pretrained_models.zip“ file that is provided in the present zenodo page contains a set of pretrained models of the CA-SUM network architecture. After downloading and unpacking this file, in the created “pretrained_models” folder, you will find two sub-directories one per each of the utilized benchmarking datasets (SumMe and TVSum) in our experimental evaluations. Within each of these sub-directories we provide the pretrained model (.pt file) for each data-split (split0-split4), where the naming of the provided .pt file indicates the training epoch and the value of the length regularization factor of the selected pretrained model.</p> <p>The models have been trained in a full-batch mode (i.e., batch size is equal to the number of training samples) and were automatically selected after the end of the training process, based on a methodology that relies on transductive inference (described in Section 4.2 of [1]). Finally, the data-splits we used for performing inference on the provided pretrained models, and the source code that can be used for training your own models of the proposed CA-SUM network architecture, can be found at: <a href="https://github.com/e-apostolidis/CA-SUM">https://github.com/e-apostolidis/CA-SUM</a>.</p> <p><strong>License and Citation:</strong></p> <p>These resources are provided for academic, non-commercial use only. If you find these resources useful in your work, please cite the following publication where they are introduced:</p> <p>E. Apostolidis, G. Balaouras, V. Mezaris, and I. Patras. 2022, “Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video Frames”, Proc. of the 2022 Int. Conf. on Multimedia Retrieval (ICMR ’22), June 2022, Newark, NJ, USA. <a href="https://doi.org/10.1145/3512527.3531404">https://doi.org/10.1145/3512527.3531404</a> Software available at: <a href="https://github.com/e-apostolidis/CA-SUM">https://github.com/e-apostolidis/CA-SUM</a></p>
CA-HF65-3 - CA-HF187-VASIJA01
Ollas de estilo Ichsma. Cultura Ichma - Inca (Horizonte Tardío 1476 - 1532 d.C). Registro y digitalización 3D: Lic. Jesus Bahamonde Schreiber. Source: Objaverse 1.0 / Sketchfab
Colorful Mural in Long Beach, CA
Love how Long Beach has a bunch of artful murals throughout its city. Here's one I scanned with Trnio Plus using LiDar capture. Source: Objaverse 1.0 / Sketchfab
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.